This paper proposes a novel extractive dividing wall distillation column, which has been designed using a constrained stochastic multiobjective optimization technique. The approach is based on the use of genetic algorithms to determine the design that minimizes energy consumption and total annualized cost. Several case studies are used to show the feasibility of performing extractive separations in dividing wall distillation columns. The simulation results show the effect of the main variables on the complex extractive distillation process.

No abstract

The optimal design of dividing wall columns is a non-linear and multivariable problem, and the objective function used as optimization criterion is generally non-convex with several local optimums. Considering this fact, in this paper, we studied the design of dividing wall columns using as a design tool, a multi-objective genetic algorithm with restrictions, written in Matlab TM and using the process simulator Aspen Plus TM for the evaluation of the objective function. Numerical performance of this method has been tested in the design of columns with one or two dividing walls and with several mixtures to test the effect of the relative volatilities of the feed mixtures on energy consumption, second law efficiency, total annual cost, and theoretical control properties. In general, the numerical performance shows that this method appears to be robust and suitable for the design of sequences with dividing walls.

a b s t r a c tReactive separation processes were recently proposed for the synthesis of fatty acid methyl esters (FAMEs), most of them making use of solid catalysts thus eliminating all conventional catalyst-related operations, improving process efficiency and reducing energy requirements. Such integrated systems require a stoichiometric reactants ratio in order to achieve complete conversion and high purity products. However, maintaining this ratio can be very difficult in practice, especially when the fatty acids feed composition is not constant in time.This study proposes a novel biodiesel process based on a reactive dividing-wall column (R-DWC) that allows the use of only $15% excess of methanol to completely convert the fatty acids feedstock. FAME are produced as pure bottom product, water as side stream, while the methanol excess is recovered as top distillate and recycled. The design is a challenging global optimization problem with discrete and continuous decision variables. The optimal setup was established by using simulated annealing as optimization method implemented in Matlab, and coupled with rigorous simulations carried out in Aspen Plus. Along with the FAME production, the novel design alternatives allow a fortune to be saved by reducing the energy requirements with over 25% and by using less equipment units than conventional processes.